Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
deberta
text-classification
Instructions to use cross-encoder/nli-deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-deberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-deberta-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cross-encoder/nli-deberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-deberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cross-encoder/nli-deberta-base: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/cross-encoder/nli-deberta-base/resolve/094ba40cf71a4fc3fec6acfb5045637d47b55418/pytorch_model.bin
- Command line
-
hf download hf://cross-encoder/nli-deberta-base@094ba40cf71a4fc3fec6acfb5045637d47b55418/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cross-encoder/nli-deberta-base/resolve/094ba40cf71a4fc3fec6acfb5045637d47b55418/pytorch_model.bin
557 MB
- Xet hash:
- 6f1e3b8325fa22f5f851463880b24ee4149c159379f2005122649f1fc9f9cc2a
- Size of remote file:
- 557 MB
- SHA256:
- 9eeedc383e1233cdff985aab798fd099c8444a2208620a95101395938cd5c307
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